Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four

23 February 2022

Papers citing "Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four"

35 / 35 papers shown

Title
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings Jan Macdonald Mathieu Besançon Sebastian Pokutta 49 12 0 15 Oct 2021
Quality Metrics for Transparent Machine Learning With and Without Humans In the Loop Are Not Correlated F. Biessmann D. Refiano 38 10 0 01 Jul 2021
Counterfactual Explanations Can Be Manipulated Dylan Slack Sophie Hilgard Himabindu Lakkaraju Sameer Singh 62 137 0 04 Jun 2021
Adaptive Warm-Start MCTS in AlphaZero-like Deep Reinforcement Learning Hui Wang Mike Preuss Aske Plaat AI4CE 27 9 0 13 May 2021
Sampling Permutations for Shapley Value Estimation Rory Mitchell Joshua N. Cooper E. Frank G. Holmes 50 119 0 25 Apr 2021
Evaluating Explanations: How much do explanations from the teacher aid students? Danish Pruthi Rachit Bansal Bhuwan Dhingra Livio Baldini Soares Michael Collins Zachary Chase Lipton Graham Neubig William W. Cohen FAtt XAI 46 109 0 01 Dec 2020
Deep Neural Network Training with Frank-Wolfe Sebastian Pokutta Christoph Spiegel Max Zimmer 47 27 0 14 Oct 2020
Fairwashing Explanations with Off-Manifold Detergent Christopher J. Anders Plamen Pasliev Ann-Kathrin Dombrowski K. Müller Pan Kessel FAtt FaML 42 97 0 20 Jul 2020
Shapley explainability on the data manifold Christopher Frye Damien de Mijolla T. Begley Laurence Cowton Megan Stanley Ilya Feige FAtt TDI 33 99 0 01 Jun 2020
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 43 132 0 20 Dec 2019
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 68 817 0 06 Nov 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 116 6,251 0 22 Oct 2019
The many Shapley values for model explanation Mukund Sundararajan A. Najmi TDI FAtt 58 632 0 22 Aug 2019
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 78 330 0 19 Jun 2019
A Rate-Distortion Framework for Explaining Neural Network Decisions Jan Macdonald S. Wäldchen Sascha Hauch Gitta Kutyniok 44 40 0 27 May 2019
Towards Efficient Data Valuation Based on the Shapley Value R. Jia David Dao Wei Ping F. Hubis Nicholas Hynes Nezihe Merve Gürel Yue Liu Ce Zhang D. Song C. Spanos TDI 63 412 0 27 Feb 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 84 1,009 0 26 Feb 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 93 202 0 06 Feb 2019
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 123 1,963 0 08 Oct 2018
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations Weili Nie Yang Zhang Ankit B. Patel FAtt 120 151 0 18 May 2018
A Symbolic Approach to Explaining Bayesian Network Classifiers Andy Shih Arthur Choi Adnan Darwiche FAtt 64 243 0 09 May 2018
What do we need to build explainable AI systems for the medical domain? Andreas Holzinger Chris Biemann C. Pattichis D. Kell 66 689 0 28 Dec 2017
Proximal Policy Optimization Algorithms John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov OffRL 446 18,931 0 20 Jul 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 199 2,221 0 12 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.0K 21,815 0 22 May 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 74 1,517 0 11 Apr 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 184 3,865 0 10 Apr 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 376 3,776 0 28 Feb 2017
Deep Reinforcement Learning: An Overview Yuxi Li OffRL VLM 155 1,530 0 25 Jan 2017
The Latin American Giant Observatory: a successful collaboration in Latin America based on Cosmic Rays and computer science domains Hernán Asorey R. Mayo-García L. Núñez M. Pascual A. J. Rubio-Montero M. Suárez-Durán L. A. Torres-Niño 81 5 0 30 May 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.1K 16,931 0 16 Feb 2016
Evaluating the visualization of what a Deep Neural Network has learned Wojciech Samek Alexander Binder G. Montavon Sebastian Lapuschkin K. Müller XAI 132 1,191 0 21 Sep 2015
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 236 4,665 0 21 Dec 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 297 7,289 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 546 15,874 0 12 Nov 2013